• Journal of the Korea Concrete Institute
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• v.14 no.6
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• pp.934-941
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• 2002

It is important to consider the effect of member size when estimating the ultimate strength of a concrete flexural member because the strength always decreases with an increase of member size. In this study, the size effect of a reinforced concrete (RC) beam was experimentally investigated. For this purpose, a series of beam specimens subjected to four-point loading were tested. More specifically, three different effective depth (d$\approx$15, 30, and 60 cm) reinforced concrete beams were tested to investigate the size effect. The shear-span to depth ratio (a/d=3) and thickness (20 cm) of the specimens were kept constant where the size effect in out-of-plane direction is not considered. The test results are curve fitted using least square method (LSM) to obtain parameters for the modified size effect law (MSEL). The analysis results show that the flexural compressive strength and the ultimate strain decrease as the specimen size increases. In the future study, since $\beta_1$ value suggested by design code and ultimate strain change with specimen size variation, a more detailed analysis should be performed. Finally, parameters for MSEL are also suggested.

• Journal of the Korea Concrete Institute
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• v.26 no.3
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• pp.315-321
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• 2014

Concrete as a construction material is widely used in nuclear vessel and plant for excellent radiation shielding. However the isolation characteristics in concrete may affect adversely in the case of fire and melt-down in nuclear vessel since temperature cooling down is very difficult from outside. This study is for development of high thermal conductive concrete, and its mechanical and thermal properties are evaluated. Magnetite aggregates with volume ratio of 42.3% (maximum) and steel powder of 1.5% are replaced with normal aggregates and thermal properties are evaluated. Thermal conductivity little increases by 30% addition of magnetite but rapidly increases afterwards. Finally thermal conductivity is magnified to 2.5 times in the case of 42.3% addition of magnetite. Steel powder has a positive effect on high thermal conduction to 106~113%. Several models for thermal conduction like ACI, DEMM, and MEM are compared with test results and they are verified to reasonably predict the thermal conductivity with increasing addition of magnetite aggregates and steel powder.

• Spatial Information Research
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• v.15 no.2
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• pp.111-121
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• 2007

The GPS positioning offer 3D position using code and carrier phase measurements, but the user can obtain the precise accuracy positioning using carrier phase in Real Time Kinematic(RTK). The main problem, which RTK have to overcome, is the necessary to have a reference station(RS) when using RTK should be generally no more than 10km on average, which is significantly different from DGPS, where distances to RS can exceed several hundred kilometers. The accuracy of today's RTK is limited by the distance dependent errors from orbit, ionosphere and troposphere as well as station dependent influences like multipath and antenna phase center variations. For these reasons, the author proposes Network based GPS Carrier Phase Differential Positioning using Multiple RS which is detached from user receiver about 30km. An important part of the proposed system is algorithm and software development, named DAUNet. The main process is corrections computation, corrections interpolation and searching for the integer ambiguity. Corrections computation of satellite by satellite and epoch by epoch at each reference station are calculated by a Functional model and Stochastic model based on a linear combination algorithm and corrections interpolation at user receiver are used by area correction parameters. As results, the users can obtain the cm-level positioning.

• Journal of the Korean Society of Propulsion Engineers
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• v.15 no.4
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• pp.85-93
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• 2011

A parametric study is conducted for the design of segmented arc plasma torch with the input power and current of 0.4 MW and 300 A, respectively. For this purpose, we use the analytical relationship between input power, current condition, plasma temperature, inner diameter (R) and length (L) of the torch constrictor based on arc channel model. The results reveal that arc plasma temperatures increase monotonically as ��L increases or R decreases for the ranges of R ${\leq}$ 7.5 mm and L ${\leq}$ 1.25 m. For larger valuse of ��R and L than 7.5 mm and 1.25 m, respectively, however, they show non-linear behavior corresponding to the variations of ��L, which stands for the generation of unstable arc plasma. From this parametric study, optimum ranges of R and L are suggested as 5.5 mm ${\leq}$ R ${\leq}$ 7.5 mm and 0.25 m ${\leq}$ L ${\leq}$ 0.5 m for 0.4 MW class segmented arc plasma torch, under which stable arc plasma can be achieved at the input currents of ~300 A.

• Journal of the Korea Concrete Institute
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• v.24 no.2
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• pp.157-164
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• 2012

This study evaluates the shear performance of reinforced concrete beams with recycled coarse aggregates. A total of six specimens with various replacement ratios of recycled coarse aggregates (0%, 50%, and 100%) and different amount of shear reinforcement were cast and tested in this study. A finite element analysis was performed to predict the shear behavior of the specimens with natural or recycled coarse aggregates. The FE analysis was performed using a two-dimensional nonlinear FE analysis program based on the disturbed stress field model (DSFM), which is an extension of the modified compression field theory (MCFT). Experimental results showed that the specimens with 50% and 100% replacement ratios of recycled coarse aggregates had the similar shear strength compared to the specimen with natural aggregates, regardless of the replacement ratios of recycled coarse aggregates and the amount of the shear reinforcement. Furthermore, the comparison between experimental and analytical results showed that the proposed numerical modeling methods and the analytical model, DSFM, can be successfully used to predict the shear behavior of reinforced concrete beams with recycled coarse aggregates.

• Proceedings of KOSOMES biannual meeting
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• 2005.05a
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• pp.141-146
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• 2005

Ship have cutout inner bottom and girder and floor etc. Ship's structure is used much, and structure strength must be situated, but establish new concept when high stress interacts sometimes fatally the area. There is no big problem usually by aim of weight reduction, a person and change of freight, piping etc. Because cutout's existence grow up in this place, and, elastic buckling strength by load causes large effect in ultimate strength. Therefore, stiffened perforated plate considering buckling strength and ultimate strength is one of important design criteria which must examine when decide structural concept at initial design. Therefore, and, reasonable buckling strength about perforated stiffened plate need to ultimate strength limited design . Calculated ultimate strength varied several web height and cutout's dimension, and thickness in this investigated data. Used program(ANSYS) applied F.E.A code based on finite element method.

• Resources Recycling
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• v.29 no.3
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• pp.11-23
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• 2020

This study aims to investigate breakage and liberation characteristics of iron ore from Shinyemi mine, Jeongseon by ball mill. Parameters of breakage functions for three grade samples of iron ore were obtained using single-sized-feed breakage test and back-calculation based on nonlinear programming. The results showed that with the increase in the grade of iron ore, the breakage rate factor decrease whereas the particle size sensitivity decreases. This results from retardation of microcrack-propagation by magnetite grain in the ore. Breakage distribution analysis showed that the breakage mechanism appear to be impact fracture dominant with the increase of grade owing to the stress distribution effect by magnetite grain. Degree of liberation (DOL) increased with the increase in grade and decrease in particle size, respectively. Using the breakage function and size-DOL relationship, a model that can predict time-dependent-DOL is established. When scale-up factors from operating condition are available, the model is expected to be capable of predicting size and DOL with time in actual mining process.

• Journal of Korean Society for Geospatial Information Science
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• v.24 no.1
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• pp.35-41
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• 2016

GNSS/Geoid positioning technology allows orthometric height determination using both the geoidal height calculated from geoid model and the ellipsoidal height achieved by GNSS survey. In this study, Network-RTK surveying was performed through the Benchmarks in the study area to analyze the possibility of height positioning of the Network-RTK. And the orthometric heights were calculated by applying the Korean national geoid model KNGeoid13 according to the condition of with site calibration and without site calibration and the results were compared. Simplex algorithm was adopted for liner programming in this study and the heights of all Benchmarks were calculated in both case of applying site calibration and does not applying site calibration. The results were compared to Benchmark official height of the National Geographic Information Institute. The results showed that the average value of the height difference was 0.060m, and the standard deviation was 0.072m in Network-RTK without site calibration and the average value of the height difference was 0.040m, and the standard deviation was 0.047m in Network-RTK with the application of the site calibration. With linearization method to obtain the optimal solution for observations it showed that the height determination within 0.033m was available in GNSS Network-RTK positioning.

• Journal of Intelligence and Information Systems
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• v.28 no.4
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• pp.119-133
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• 2022

One of the most important current features of food related industry is the growth of food delivery service. Another notable food related culture is, with the advent of Youtube, the popularity of Mukbang, which refers to content that records eating. Based on these background, this study intended to focus on two things. First, we tried to see the impact of Youtube Mukbang and the sentiments of Mukbang comments on the number of related food deliveries. Next, we tried to set up the predictive modeling of chicken delivery order with machine learning method. We used Youtube Mukbang comments data as well as weather related data as main independent variables. The dependent variable used in this study is the number of delivery order of fried chicken. The period of data used in this study is from June 3, 2015 to September 30, 2019, and a total of 1,580 data were used. For the predictive modeling, we used machine learning methods such as linear regression, ridge, lasso, random forest, and gradient boost. We found that the sentiment of Youtube Mukbang and comments have impacts on the number of delivery orders. The prediction model with Mukban data we set up in this study had better performances than the existing models without Mukbang data. We also tried to suggest managerial implications to the food delivery service industry.

• Journal of Intelligence and Information Systems
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• v.29 no.3
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• pp.419-437
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• 2023

As the number of thin filers in Korea surpasses 12 million, there is a growing interest in enhancing the accuracy of assessing their credit default risk to generate additional revenue. Specifically, researchers are actively pursuing the development of default prediction models using machine learning and deep learning algorithms, in contrast to traditional statistical default prediction methods, which struggle to capture nonlinearity. Among these efforts, Graph Neural Network (GNN) architecture is noteworthy for predicting default in situations with limited data on thin filers. This is due to their ability to incorporate network information between borrowers alongside conventional credit-related data. However, prior research employing graph neural networks has faced limitations in effectively handling diverse categorical variables present in credit information. In this study, we introduce the Transformer embedded Graph Convolutional Network (TeGCN), which aims to address these limitations and enable effective default prediction for thin filers. TeGCN combines the TabTransformer, capable of extracting contextual information from categorical variables, with the Graph Convolutional Network, which captures network information between borrowers. Our TeGCN model surpasses the baseline model's performance across both the general borrower dataset and the thin filer dataset. Specially, our model performs outstanding results in thin filer default prediction. This study achieves high default prediction accuracy by a model structure tailored to characteristics of credit information containing numerous categorical variables, especially in the context of thin filers with limited data. Our study can contribute to resolving the financial exclusion issues faced by thin filers and facilitate additional revenue within the financial industry.